System and method for automated message delivery prioritization

Patent metadata

Field	Value
Publication number	US-12101287-B2
Application number	US-202318474188-A
Country	US
Kind code	B2
Filing date	Sep 25, 2023
Priority date	Mar 26, 2021
Publication date	Sep 24, 2024
Grant date	Sep 24, 2024

Abstract

Systems and methods for message delivery prioritization that can include receiving, via an application programming interface, a messaging request of an entity to transmit one or more messages to a plurality of users, selecting one or more message transmission options based on message-associated delivery attributes, and causing the one or more messages to be transmitted to the plurality of users using the selected one or more message transmission options.

First claim

What is claimed is:

1. A method comprising:
receiving, via an application programming interface, a messaging request of an entity to transmit one or more messages to a plurality of users, the messaging request being associated with message-associated delivery attributes comprising two or more of a timing priority attribute, a reliability priority attribute, a content quality attribute or a message delivery cost attribute for transmitting the one or more messages to the plurality of users;
selecting one or more message transmission options using a machine learning model and based on the message-associated delivery attributes, the selected one or more message transmission options comprising at least one of a messaging channel or a messaging route for transmitting the one or more messages to the plurality of users;
causing the one or more messages to be transmitted to the plurality of users using the selected one or more message transmission options;
receiving delivery feedback after transmission of the one or more messages to the plurality of users; and
re-training the machine learning model based on the delivery feedback.

2. The method of claim 1 , further comprising:
predicting, based on analysis of the message request, at least one of the message-associated delivery attributes, wherein selecting the one or more message transmission options is based on the at least one predicted message-associated delivery attribute.

3. The method of claim 2 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
detecting a messaging time window of the messaging request.

4. The method of claim 3 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
identifying the message request as an interaction message indicating that the one or more messages are to be transmitted with high timing priority.

5. The method of claim 1 , wherein selecting the one or more message transmission options using the machine learning model and based on the message-associated delivery attributes further comprises:
applying the machine learning model to the message-associated delivery attributes; and
obtaining an output of the machine learning model, the output indicating the one or more message transmission options.

6. The method of claim 5 , further comprising training the machine learning model.

7. A system comprising:
a memory; and
one or more processors, coupled to the memory, to perform operations comprising:
receiving, via an application programming interface, a messaging request of an entity to transmit one or more messages to a plurality of users, the messaging request being associated with message-associated delivery attributes comprising two or more of a timing priority attribute, a reliability priority attribute, a content quality attribute or a message delivery cost attribute for transmitting the one or more messages to the plurality of users;
selecting one or more message transmission options using a machine learning model and based on the message-associated delivery attributes, the selected one or more message transmission options comprising at least one of a messaging channel or a messaging route for transmitting the one or more messages to the plurality of users;
causing the one or more messages to be transmitted to the plurality of users using the selected one or more message transmission options;
receiving delivery feedback after transmission of the one or more messages to the plurality of users; and
re-training the machine learning model based on the delivery feedback.

8. The system of claim 7 , the operations further comprising:
predicting, based on analysis of the message request, at least one of the message-associated delivery attributes, wherein selecting the one or more message transmission options is based on the at least one predicted message-associated delivery attribute.

9. The system of claim 8 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
detecting a messaging time window of the messaging request.

10. The system of claim 9 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
identifying the message request as an interaction message indicating that the one or more messages are to be transmitted with high timing priority.

11. The system of claim 7 , wherein selecting the one or more message transmission options using the machine learning model and based on the message-associated delivery attributes further comprises:
applying the machine learning model to the message-associated delivery attributes; and
obtaining an output of the machine learning model, the output indicating the one or more message transmission options.

12. The system of claim 11 , the operations further comprising training the machine learning model.

13. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving, via an application programming interface, a messaging request of an entity to transmit one or more messages to a plurality of users, the messaging request being associated with message-associated delivery attributes comprising two or more of a timing priority attribute, a reliability priority attribute, a content quality attribute or a message delivery cost attribute for transmitting the one or more messages to the plurality of users;
selecting one or more message transmission options using a machine learning model and based on the message-associated delivery attributes, the selected one or more message transmission options comprising at least one of a messaging channel or a messaging route for transmitting the one or more messages to the plurality of users;
causing the one or more messages to be transmitted to the plurality of users using the selected one or more message transmission options;
receiving delivery feedback after transmission of the one or more messages to the plurality of users; and
re-training the machine learning model based on the delivery feedback.

14. The non-transitory computer-readable medium of claim 13 , the operations further comprising:
predicting, based on analysis of the message request, at least one of the message-associated delivery attributes, wherein selecting the one or more message transmission options is based on the at least one predicted message-associated delivery attribute.

15. The non-transitory computer-readable medium of claim 14 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
detecting a messaging time window of the messaging request.

16. The non-transitory computer-readable medium of claim 15 , wherein predicting, based on the analysis of the message request, the at least one of the message-associated delivery attributes further comprises:
identifying the message request as an interaction message indicating that the one or more messages are to be transmitted with high timing priority.

17. The non-transitory computer-readable medium of claim 13 , wherein selecting the one or more message transmission options using the machine learning model and based on the message-associated delivery attributes further comprises:
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Assignees

• Twilio Inc

Inventors

• Agarwal Amit
• Tan Peter
• Nazari Nariman

Classifications

• H04L67/62 Primary

  Establishing a time schedule for servicing the requests · CPC title

• H04L51/214

  using selective forwarding · CPC title

• H04L51/046

  Interoperability with other network applications or services · CPC title

• H04L51/226 Primary

  Delivery according to priorities · CPC title